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На примере стратегии SimpleMovingAverage сделаем загрузку с polo

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import datetime  # For datetime objects
import os.path  # To manage paths
import sys  # To find out the script name (in argv[0]) 

# Import the backtrader platform
import backtrader as bt 

import requests
import json
import time
import math
from datetime import datetime
import pandas as pd
 

def get_polonix() :
    time_depth = 500
    start_day = 500
    st_time=time.time()-start_day*24*60*60
    end_time=st_time+time_depth*60*60*24
    pair = 'USDT_BTC'

    #resource=requests.get("https://poloniex.com/public?command=returnChartData&currencyPair=%s&start=%s&end=%s&period=1800" % (pair,st_time,end_time))
    resource=requests.get("https://poloniex.com/public?command=returnChartData&currencyPair=%s&start=%s&end=%s&period=14400" % (pair,st_time,end_time))
    #resource=requests.get("https://poloniex.com/public?command=returnChartData&currencyPair=%s&start=%s&end=%s&period=300" % (pair,st_time,end_time))
    #resource=requests.get("https://poloniex.com/public?command=returnChartData&currencyPair=%s&start=%s&end=%s&period=86400" % (pair,st_time,end_time))
    #resource=requests.get("https://poloniex.com/public?command=returnChartData&currencyPair=%s&start=%s&end=%s&period=21600" % (pair,st_time,end_time))

    data=[]
    chart_data={}
    chart_data = json.loads(resource.text)
    for elems in chart_data:
        data.append(elems)

    df = pd.DataFrame(data, columns=['date', 'open', 'high', 'low', 'close', 'volume'])
    df['openinterest']=0
    df['date'] = pd.to_datetime(df['date'], unit='s')
    #df = df[(df['date'] > '2018-1-1') & (df['date'] <= '2018-2-1')]
    #df = df[(df['date'] > '2017-9-1') & (df['date'] <= '2018-1-1')]
    df = df[(df['date'] >= '2018-4-1')]
    df = df.set_index('date')
    #print(df)
    return df
 

 # Create a Stratey
class TestStrategy(bt.Strategy):
    params = (
        ('maperiod', 15),
    )

    def log(self, txt, dt=None):
        ''' Logging function fot this strategy'''
        dt = dt or self.datas[0].datetime.date(0)
        #print('%s, %s' % (dt.isoformat(), txt))

    def __init__(self):
        # Keep a reference to the "close" line in the data[0] dataseries
        self.dataclose = self.datas[0].close

        # To keep track of pending orders and buy price/commission
        self.order = None
        self.buyprice = None
        self.buycomm = None

        # Add a MovingAverageSimple indicator
        self.sma = bt.indicators.SimpleMovingAverage(
            self.datas[0], period=self.params.maperiod)

    def notify_order(self, order):
        if order.status in [order.Submitted, order.Accepted]:
            # Buy/Sell order submitted/accepted to/by broker - Nothing to do
            return

        # Check if an order has been completed
        # Attention: broker could reject order if not enough cash
        if order.status in [order.Completed]:
            if order.isbuy():
                self.log(
                    'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
                    (order.executed.price,
                     order.executed.value,
                     order.executed.comm))

                self.buyprice = order.executed.price
                self.buycomm = order.executed.comm
            else:  # Sell
                self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
                         (order.executed.price,
                          order.executed.value,
                          order.executed.comm))

            self.bar_executed = len(self)

        elif order.status in [order.Canceled, order.Margin, order.Rejected]:
            self.log('Order Canceled/Margin/Rejected')

        self.order = None

    def notify_trade(self, trade):
        if not trade.isclosed:
            return

        self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %
                 (trade.pnl, trade.pnlcomm))

    def next(self):
        # Simply log the closing price of the series from the reference
        self.log('Close, %.2f' % self.dataclose[0])

        # Check if an order is pending ... if yes, we cannot send a 2nd one
        if self.order:
            return

        # Check if we are in the market
        if not self.position:

            # Not yet ... we MIGHT BUY if ...
            if self.dataclose[0] > self.sma[0]:

                # BUY, BUY, BUY!!! (with all possible default parameters)
                self.log('BUY CREATE, %.2f' % self.dataclose[0])

                # Keep track of the created order to avoid a 2nd order
                self.order = self.buy()

        else:

            if self.dataclose[0] < self.sma[0]:
                # SELL, SELL, SELL!!! (with all possible default parameters)
                self.log('SELL CREATE, %.2f' % self.dataclose[0])

                # Keep track of the created order to avoid a 2nd order
                self.order = self.sell()


if __name__ == '__main__':
    # Create a cerebro entity
    cerebro = bt.Cerebro()

    # Add a strategy
    cerebro.addstrategy(TestStrategy)

    # Datas are in a subfolder of the samples. Need to find where the script is
    # because it could have been called from anywhere
    #modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
    #datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt')

    # Create a Data Feed
    #data = bt.feeds.YahooFinanceCSVData(
        #dataname=datapath,
        # Do not pass values before this date
        #fromdate=datetime.datetime(2000, 1, 1),
        # Do not pass values before this date
        #todate=datetime.datetime(2000, 12, 31),
        # Do not pass values after this date
        #reverse=False) 

    df = get_polonix()
    data = bt.feeds.PandasData(dataname=df)
 

    # Add the Data Feed to Cerebro
    cerebro.adddata(data)

    # Set our desired cash start
    cerebro.broker.setcash(100000.0)

    # Add a FixedSize sizer according to the stake
    cerebro.addsizer(bt.sizers.FixedSize, stake=10)

    # Set the commission
    cerebro.broker.setcommission(commission=0.0)

    # Print out the starting conditions
    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())

    # Run over everything
    cerebro.run()

    # Print out the final result
    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())

    # Finally plot the end results 

    cerebro.plot(style='candlestick')